Abstract

Quantification of the human rod and cone photoreceptor mosaic in adaptive optics scanning light ophthalmoscope (AOSLO) images is useful for the study of various retinal pathologies. Subjective and time-consuming manual grading has remained the gold standard for evaluating these images, with no well validated automatic methods for detecting individual rods having been developed. We present a novel deep learning based automatic method, called the rod and cone CNN (RAC-CNN), for detecting and classifying rods and cones in multimodal AOSLO images. We test our method on images from healthy subjects as well as subjects with achromatopsia over a range of retinal eccentricities. We show that our method is on par with human grading for detecting rods and cones.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (4)

S. A. Burns, A. E. Elsner, K. A. Sapoznik, R. L. Warner, and T. J. Gast, “Adaptive optics imaging of the human retina,” Prog. Retinal Eye Res. 68, 1–30 (2019).
[Crossref]

T. B. DuBose, F. LaRocca, S. Farsiu, and J. A. Izatt, “Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy,” Nat. Photonics 13(4), 257–262 (2019).
[Crossref]

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
[Crossref]

S. Soltanian-Zadeh, K. Sahingur, S. Blau, Y. Gong, and S. Farsiu, “Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(17), 8554–8563 (2019).
[Crossref]

2018 (8)

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Automatic cone photoreceptor localisation in healthy and stargardt afflicted retinas using deep learning,” Sci. Rep. 8(1), 7911 (2018).
[Crossref]

N. Sredar, O. E. Fagbemi, and A. Dubra, “Sub-airy confocal adaptive optics scanning ophthalmoscopy,” Transl. Vis. Sci. Techn. 7(2), 17 (2018).
[Crossref]

K. A. Sapoznik, T. Luo, A. de Castro, L. Sawides, R. L. Warner, and S. A. Burns, “Enhanced retinal vasculature imaging with a rapidly configurable aperture,” Biomed. Opt. Express 9(3), 1323–1333 (2018).
[Crossref]

J. Loo, L. Fang, D. Cunefare, G. J. Jaffe, and S. Farsiu, “Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2,” Biomed. Opt. Express 9(6), 2681–2698 (2018).
[Crossref]

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
[Crossref]

T. DuBose, D. Nankivil, F. LaRocca, G. Waterman, K. Hagan, J. Polans, B. Keller, D. Tran-Viet, L. Vajzovic, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Handheld adaptive optics scanning laser ophthalmoscope,” Optica 5(9), 1027–1036 (2018).
[Crossref]

M. Heisler, M. J. Ju, M. Bhalla, N. Schuck, A. Athwal, E. V. Navajas, M. F. Beg, and M. V. Sarunic, “Automated identification of cone photoreceptors in adaptive optics optical coherence tomography images using transfer learning,” Biomed. Opt. Express 9(11), 5353–5367 (2018).
[Crossref]

A. D. Desai, C. Peng, L. Fang, D. Mukherjee, A. Yeung, S. J. Jaffe, J. B. Griffin, and S. Farsiu, “Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging,” Biomed. Opt. Express 9(12), 6038–6052 (2018).
[Crossref]

2017 (11)

J. Polans, D. Cunefare, E. Cole, B. Keller, P. S. Mettu, S. W. Cousins, M. J. Allingham, J. A. Izatt, and S. Farsiu, “Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics optical coherence tomography angiography in diabetic patients,” Opt. Lett. 42(1), 17–20 (2017).
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S. P. K. Karri, D. Chakraborty, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomed. Opt. Express 8(2), 579–592 (2017).
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J. A. Feeks and J. J. Hunter, “Adaptive optics two-photon excited fluorescence lifetime imaging ophthalmoscopy of exogenous fluorophores in mice,” Biomed. Opt. Express 8(5), 2483–2495 (2017).
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L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
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C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
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A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
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X. Fei, J. Zhao, H. Zhao, D. Yun, and Y. Zhang, “Deblurring adaptive optics retinal images using deep convolutional neural networks,” Biomed. Opt. Express 8(12), 5675–5687 (2017).
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E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U. S. A. 114(3), 586–591 (2017).
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Z. Liu, K. Kurokawa, F. Zhang, J. J. Lee, and D. T. Miller, “Imaging and quantifying ganglion cells and other transparent neurons in the living human retina,” Proc. Natl. Acad. Sci. U. S. A. 114(48), 12803–12808 (2017).
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D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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J. Liu, H. Jung, A. Dubra, and J. Tam, “Automated photoreceptor cell identification on nonconfocal adaptive optics images using multiscale circular voting,” Invest. Ophthalmol. Visual Sci. 58(11), 4477–4489 (2017).
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2016 (8)

R. Sharma, D. R. Williams, G. Palczewska, K. Palczewski, and J. J. Hunter, “Two-photon autofluorescence imaging reveals cellular structures throughout the retina of the living primate eyetwo-photon autofluorescence imaging,” Invest. Ophthalmol. Visual Sci. 57(2), 632–646 (2016).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, and J. Cuadros, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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M. J. van Grinsven, B. van Ginneken, C. B. Hoyng, T. Theelen, and C. I. Sánchez, “Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images,” IEEE Trans. Med. Imag. 35(5), 1273–1284 (2016).
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M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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P. Liskowski and K. Krawiec, “Segmenting retinal blood vessels with deep neural networks,” IEEE Trans. Med. Imag. 35(11), 2369–2380 (2016).
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Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imag. 35(1), 109–118 (2016).
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C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
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D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
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2015 (4)

L. Mariotti and N. Devaney, “Performance analysis of cone detection algorithms,” J. Opt. Soc. Am. A 32(4), 497–506 (2015).
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D. M. Bukowska, A. L. Chew, E. Huynh, I. Kashani, S. L. Wan, P. M. Wan, and F. K. Chen, “Semi-automated identification of cones in the human retina using circle hough transform,” Biomed. Opt. Express 6(12), 4676–4693 (2015).
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H. Song, E. A. Rossi, L. Latchney, A. Bessette, E. Stone, J. J. Hunter, D. R. Williams, and M. Chung, “Cone and rod loss in stargardt disease revealed by adaptive optics scanning light ophthalmoscopydistribution of cone and rod loss in stargardt diseasedistribution of cone and rod loss in stargardt disease,” JAMA Ophthalmol. 133(10), 1198–1203 (2015).
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A. Roorda and J. L. Duncan, “Adaptive optics ophthalmoscopy,” Annu. Rev. Vis. Sci. 1(1), 19–50 (2015).
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2014 (3)

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Visual Sci. 55(7), 4244–4251 (2014).
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M. Lombardo, S. Serrao, and G. Lombardo, “Technical factors influencing cone packing density estimates in adaptive optics flood illuminated retinal images,” PLoS One 9(9), e107402 (2014).
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Y. Jian, J. Xu, M. A. Gradowski, S. Bonora, R. J. Zawadzki, and M. V. Sarunic, “Wavefront sensorless adaptive optics optical coherence tomography for in vivo retinal imaging in mice,” Biomed. Opt. Express 5(2), 547–559 (2014).
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2013 (3)

2012 (3)

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref]

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
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2011 (6)

J. J. Hunter, B. Masella, A. Dubra, R. Sharma, L. Yin, W. H. Merigan, G. Palczewska, K. Palczewski, and D. R. Williams, “Images of photoreceptors in living primate eyes using adaptive optics two-photon ophthalmoscopy,” Biomed. Opt. Express 2(1), 139–148 (2011).
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O. P. Kocaoglu, S. Lee, R. S. Jonnal, Q. Wang, A. E. Herde, J. C. Derby, W. Gao, and D. T. Miller, “Imaging cone photoreceptors in three dimensions and in time using ultrahigh resolution optical coherence tomography with adaptive optics,” Biomed. Opt. Express 2(4), 748–763 (2011).
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A. Dubra and Y. Sulai, “Reflective afocal broadband adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(6), 1757–1768 (2011).
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A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
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D. Merino, J. L. Duncan, P. Tiruveedhula, and A. Roorda, “Observation of cone and rod photoreceptors in normal subjects and patients using a new generation adaptive optics scanning laser ophthalmoscope,” Biomed. Opt. Express 2(8), 2189–2201 (2011).
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R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express 2(9), 2577–2589 (2011).
[Crossref]

2009 (1)

2008 (1)

2007 (2)

2006 (1)

2005 (1)

2004 (1)

2002 (1)

1999 (1)

A. Roorda and D. R. Williams, “The arrangement of the three cone classes in the living human eye,” Nature 397(6719), 520–522 (1999).
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1997 (1)

1990 (1)

C. A. Curcio, K. R. Sloan, R. E. Kalina, and A. E. Hendrickson, “Human photoreceptor topography,” J. Comp. Neurol. 292(4), 497–523 (1990).
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1948 (1)

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1945 (1)

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Abozaid, M. A.

M. A. Abozaid, C. S. Langlo, A. M. Dubis, M. Michaelides, S. Tarima, and J. Carroll, “Reliability and repeatability of cone density measurements in patients with congenital achromatopsia,” in Advances in Experimental Medicine and Biology, C. Bowes Rickman, M. M. LaVail, R. E. Anderson, C. Grimm, J. Hollyfield, and J. Ash, eds. (Springer International Publishing, Cham, 2016), pp. 277–283.

Abràmoff, M. D.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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Anderson, R.

A. Turpin, P. Morrow, B. Scotney, R. Anderson, and C. Wolsley, “Automated identification of photoreceptor cones using multi-scale modelling and normalized cross-correlation,” in Image analysis and processing – iciap 2011, G. Maino and G. Foresti, eds. (Springer Berlin Heidelberg, 2011), pp. 494–503.

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F. Mohammad, R. Ansari, J. Wanek, and M. Shahidi, “Frequency-based local content adaptive filtering algorithm for automated photoreceptor cell density quantification,” in Proceedings of IEEE International Conference on Image Processing, (IEEE, 2012), 2325–2328.

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T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Automatic cone photoreceptor localisation in healthy and stargardt afflicted retinas using deep learning,” Sci. Rep. 8(1), 7911 (2018).
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C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
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H. Song, E. A. Rossi, L. Latchney, A. Bessette, E. Stone, J. J. Hunter, D. R. Williams, and M. Chung, “Cone and rod loss in stargardt disease revealed by adaptive optics scanning light ophthalmoscopydistribution of cone and rod loss in stargardt diseasedistribution of cone and rod loss in stargardt disease,” JAMA Ophthalmol. 133(10), 1198–1203 (2015).
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S. Soltanian-Zadeh, K. Sahingur, S. Blau, Y. Gong, and S. Farsiu, “Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(17), 8554–8563 (2019).
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D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
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T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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S. A. Burns, A. E. Elsner, K. A. Sapoznik, R. L. Warner, and T. J. Gast, “Adaptive optics imaging of the human retina,” Prog. Retinal Eye Res. 68, 1–30 (2019).
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K. A. Sapoznik, T. Luo, A. de Castro, L. Sawides, R. L. Warner, and S. A. Burns, “Enhanced retinal vasculature imaging with a rapidly configurable aperture,” Biomed. Opt. Express 9(3), 1323–1333 (2018).
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D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
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B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Automatic cone photoreceptor localisation in healthy and stargardt afflicted retinas using deep learning,” Sci. Rep. 8(1), 7911 (2018).
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D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
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C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
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D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
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D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Visual Sci. 55(7), 4244–4251 (2014).
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R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (Yellott's ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33(4), 540–549 (2013).
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S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
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A. Pinhas, M. Dubow, N. Shah, T. Y. Chui, D. Scoles, Y. N. Sulai, R. Weitz, J. B. Walsh, J. Carroll, A. Dubra, and R. B. Rosen, “In vivo imaging of human retinal microvasculature using adaptive optics scanning light ophthalmoscope fluorescein angiography,” Biomed. Opt. Express 4(8), 1305–1317 (2013).
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R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
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R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express 2(9), 2577–2589 (2011).
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A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
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C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
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H. Song, E. A. Rossi, L. Latchney, A. Bessette, E. Stone, J. J. Hunter, D. R. Williams, and M. Chung, “Cone and rod loss in stargardt disease revealed by adaptive optics scanning light ophthalmoscopydistribution of cone and rod loss in stargardt diseasedistribution of cone and rod loss in stargardt disease,” JAMA Ophthalmol. 133(10), 1198–1203 (2015).
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E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U. S. A. 114(3), 586–591 (2017).
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T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
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Connor, T. B.

P. Godara, M. Wagner-Schuman, J. Rha, T. B. Connor, K. E. Stepien, and J. Carroll, “Imaging the photoreceptor mosaic with adaptive optics: Beyond counting cones,” in Retinal Degenerative Diseases (Springer US, 2012), 451–458.

Cooper, R. F.

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref]

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (Yellott's ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33(4), 540–549 (2013).
[Crossref]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref]

R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express 2(9), 2577–2589 (2011).
[Crossref]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
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Coram, M.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, and J. Cuadros, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, and J. Cuadros, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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Cunefare, D.

J. Loo, L. Fang, D. Cunefare, G. J. Jaffe, and S. Farsiu, “Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2,” Biomed. Opt. Express 9(6), 2681–2698 (2018).
[Crossref]

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
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J. Polans, D. Cunefare, E. Cole, B. Keller, P. S. Mettu, S. W. Cousins, M. J. Allingham, J. A. Izatt, and S. Farsiu, “Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics optical coherence tomography angiography in diabetic patients,” Opt. Lett. 42(1), 17–20 (2017).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
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Curcio, C. A.

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Visual Sci. 55(7), 4244–4251 (2014).
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T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Automatic cone photoreceptor localisation in healthy and stargardt afflicted retinas using deep learning,” Sci. Rep. 8(1), 7911 (2018).
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C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
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T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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Donnelly, I. I. I. W.

Dovzhenko, A.

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
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Dubis, A. M.

C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
[Crossref]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
[Crossref]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express 2(9), 2577–2589 (2011).
[Crossref]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref]

M. A. Abozaid, C. S. Langlo, A. M. Dubis, M. Michaelides, S. Tarima, and J. Carroll, “Reliability and repeatability of cone density measurements in patients with congenital achromatopsia,” in Advances in Experimental Medicine and Biology, C. Bowes Rickman, M. M. LaVail, R. E. Anderson, C. Grimm, J. Hollyfield, and J. Ash, eds. (Springer International Publishing, Cham, 2016), pp. 277–283.

DuBose, T.

DuBose, T. B.

T. B. DuBose, F. LaRocca, S. Farsiu, and J. A. Izatt, “Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy,” Nat. Photonics 13(4), 257–262 (2019).
[Crossref]

Dubow, M.

Dubra, A.

N. Sredar, O. E. Fagbemi, and A. Dubra, “Sub-airy confocal adaptive optics scanning ophthalmoscopy,” Transl. Vis. Sci. Techn. 7(2), 17 (2018).
[Crossref]

B. Davidson, A. Kalitzeos, J. Carroll, A. Dubra, S. Ourselin, M. Michaelides, and C. Bergeles, “Automatic cone photoreceptor localisation in healthy and stargardt afflicted retinas using deep learning,” Sci. Rep. 8(1), 7911 (2018).
[Crossref]

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

C. Bergeles, A. M. Dubis, B. Davidson, M. Kasilian, A. Kalitzeos, J. Carroll, A. Dubra, M. Michaelides, and S. Ourselin, “Unsupervised identification of cone photoreceptors in non-confocal adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 8(6), 3081–3094 (2017).
[Crossref]

J. Liu, H. Jung, A. Dubra, and J. Tam, “Automated photoreceptor cell identification on nonconfocal adaptive optics images using multiscale circular voting,” Invest. Ophthalmol. Visual Sci. 58(11), 4477–4489 (2017).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref]

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
[Crossref]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Visual Sci. 55(7), 4244–4251 (2014).
[Crossref]

R. F. Cooper, C. S. Langlo, A. Dubra, and J. Carroll, “Automatic detection of modal spacing (Yellott's ring) in adaptive optics scanning light ophthalmoscope images,” Ophthalmic Physiol. Opt. 33(4), 540–549 (2013).
[Crossref]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
[Crossref]

A. Pinhas, M. Dubow, N. Shah, T. Y. Chui, D. Scoles, Y. N. Sulai, R. Weitz, J. B. Walsh, J. Carroll, A. Dubra, and R. B. Rosen, “In vivo imaging of human retinal microvasculature using adaptive optics scanning light ophthalmoscope fluorescein angiography,” Biomed. Opt. Express 4(8), 1305–1317 (2013).
[Crossref]

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

K. E. Stepien, W. M. Martinez, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Subclinical photoreceptor disruption in response to severe head trauma,” Arch. Ophthalmol. 130(3), 400–402 (2012).
[Crossref]

J. J. Hunter, B. Masella, A. Dubra, R. Sharma, L. Yin, W. H. Merigan, G. Palczewska, K. Palczewski, and D. R. Williams, “Images of photoreceptors in living primate eyes using adaptive optics two-photon ophthalmoscopy,” Biomed. Opt. Express 2(1), 139–148 (2011).
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R. F. Cooper, A. M. Dubis, A. Pavaskar, J. Rha, A. Dubra, and J. Carroll, “Spatial and temporal variation of rod photoreceptor reflectance in the human retina,” Biomed. Opt. Express 2(9), 2577–2589 (2011).
[Crossref]

A. Dubra and Y. Sulai, “Reflective afocal broadband adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(6), 1757–1768 (2011).
[Crossref]

A. Dubra, Y. Sulai, J. L. Norris, R. F. Cooper, A. M. Dubis, D. R. Williams, and J. Carroll, “Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope,” Biomed. Opt. Express 2(7), 1864–1876 (2011).
[Crossref]

D. C. Gray, W. Merigan, J. I. Wolfing, B. P. Gee, J. Porter, A. Dubra, T. H. Twietmeyer, K. Ahmad, R. Tumbar, F. Reinholz, and D. R. Williams, “In vivo fluorescence imaging of primate retinal ganglion cells and retinal pigment epithelial cells,” Opt. Express 14(16), 7144–7158 (2006).
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A. Dubra and Z. Harvey, “Registration of 2D images from fast scanning ophthalmic instruments,” in Biomedical Image Registration (Springer Berlin Heidelberg, 2010), 60–71.

Duncan, J. L.

Elsner, A. E.

S. A. Burns, A. E. Elsner, K. A. Sapoznik, R. L. Warner, and T. J. Gast, “Adaptive optics imaging of the human retina,” Prog. Retinal Eye Res. 68, 1–30 (2019).
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Erginay, A.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
[Crossref]

Erker, L. R.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
[Crossref]

Fagbemi, O. E.

N. Sredar, O. E. Fagbemi, and A. Dubra, “Sub-airy confocal adaptive optics scanning ophthalmoscopy,” Transl. Vis. Sci. Techn. 7(2), 17 (2018).
[Crossref]

Falk, T.

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: Deep learning for cell counting, detection, and morphometry,” Nat. Methods 16(1), 67–70 (2019).
[Crossref]

Fang, L.

Farsiu, S.

T. B. DuBose, F. LaRocca, S. Farsiu, and J. A. Izatt, “Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy,” Nat. Photonics 13(4), 257–262 (2019).
[Crossref]

S. Soltanian-Zadeh, K. Sahingur, S. Blau, Y. Gong, and S. Farsiu, “Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(17), 8554–8563 (2019).
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A. D. Desai, C. Peng, L. Fang, D. Mukherjee, A. Yeung, S. J. Jaffe, J. B. Griffin, and S. Farsiu, “Open-source, machine and deep learning-based automated algorithm for gestational age estimation through smartphone lens imaging,” Biomed. Opt. Express 9(12), 6038–6052 (2018).
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J. Loo, L. Fang, D. Cunefare, G. J. Jaffe, and S. Farsiu, “Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2,” Biomed. Opt. Express 9(6), 2681–2698 (2018).
[Crossref]

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
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T. DuBose, D. Nankivil, F. LaRocca, G. Waterman, K. Hagan, J. Polans, B. Keller, D. Tran-Viet, L. Vajzovic, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Handheld adaptive optics scanning laser ophthalmoscope,” Optica 5(9), 1027–1036 (2018).
[Crossref]

J. Polans, D. Cunefare, E. Cole, B. Keller, P. S. Mettu, S. W. Cousins, M. J. Allingham, J. A. Izatt, and S. Farsiu, “Enhanced visualization of peripheral retinal vasculature with wavefront sensorless adaptive optics optical coherence tomography angiography in diabetic patients,” Opt. Lett. 42(1), 17–20 (2017).
[Crossref]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref]

D. Cunefare, R. F. Cooper, B. Higgins, D. F. Katz, A. Dubra, J. Carroll, and S. Farsiu, “Automatic detection of cone photoreceptors in split detector adaptive optics scanning light ophthalmoscope images,” Biomed. Opt. Express 7(5), 2036–2050 (2016).
[Crossref]

S. J. Chiu, Y. Lokhnygina, A. M. Dubis, A. Dubra, J. Carroll, J. A. Izatt, and S. Farsiu, “Automatic cone photoreceptor segmentation using graph theory and dynamic programming,” Biomed. Opt. Express 4(6), 924–937 (2013).
[Crossref]

S. J. Chiu, C. A. Toth, C. Bowes Rickman, J. A. Izatt, and S. Farsiu, “Automatic segmentation of closed-contour features in ophthalmic images using graph theory and dynamic programming,” Biomed. Opt. Express 3(5), 1127–1140 (2012).
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Feeks, J. A.

Fei, X.

Feng, B.

Q. Li, B. Feng, L. Xie, P. Liang, H. Zhang, and T. Wang, “A cross-modality learning approach for vessel segmentation in retinal images,” IEEE Trans. Med. Imag. 35(1), 109–118 (2016).
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Fercher, A. F.

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Fischer, P.

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Fischer, W.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U. S. A. 114(3), 586–591 (2017).
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Fishman, G. A.

C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
[Crossref]

D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Visual Sci. 55(7), 4244–4251 (2014).
[Crossref]

Folk, J. C.

M. D. Abràmoff, Y. Lou, A. Erginay, W. Clarida, R. Amelon, J. C. Folk, and M. Niemeijer, “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning,” Invest. Ophthalmol. Visual Sci. 57(13), 5200–5206 (2016).
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Fu, H.

H. Fu, Y. Xu, S. Lin, D. W. Kee Wong, and J. Liu, “Deepvessel: Retinal vessel segmentation via deep learning and conditional random field,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, (Springer International Publishing, 2016), 132–139.

Gao, W.

Garrioch, R.

R. Garrioch, C. Langlo, A. M. Dubis, R. F. Cooper, A. Dubra, and J. Carroll, “Repeatability of in vivo parafoveal cone density and spacing measurements,” Optom. Vis. Sci. 89(5), 632–643 (2012).
[Crossref]

Gast, T. J.

S. A. Burns, A. E. Elsner, K. A. Sapoznik, R. L. Warner, and T. J. Gast, “Adaptive optics imaging of the human retina,” Prog. Retinal Eye Res. 68, 1–30 (2019).
[Crossref]

Gee, B. P.

Godara, P.

P. Godara, M. Wagner-Schuman, J. Rha, T. B. Connor, K. E. Stepien, and J. Carroll, “Imaging the photoreceptor mosaic with adaptive optics: Beyond counting cones,” in Retinal Degenerative Diseases (Springer US, 2012), 451–458.

Gong, Y.

S. Soltanian-Zadeh, K. Sahingur, S. Blau, Y. Gong, and S. Farsiu, “Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning,” Proc. Natl. Acad. Sci. U. S. A. 116(17), 8554–8563 (2019).
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Gradowski, M. A.

Granger, C. E.

E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, T. Kawakami, W. Fischer, L. R. Latchney, J. J. Hunter, M. M. Chung, and D. R. Williams, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. U. S. A. 114(3), 586–591 (2017).
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Gray, D. C.

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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, and J. Cuadros, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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C. S. Langlo, E. J. Patterson, B. P. Higgins, P. Summerfelt, M. M. Razeen, L. R. Erker, M. Parker, F. T. Collison, G. A. Fishman, C. N. Kay, J. Zhang, R. G. Weleber, P. Yang, D. J. Wilson, M. E. Pennesi, B. L. Lam, J. Chiang, J. D. Chulay, A. Dubra, W. W. Hauswirth, and J. Carroll, “Residual foveal cone structure in cngb3-associated achromatopsia,” Invest. Ophthalmol. Visual Sci. 57(10), 3984–3995 (2016).
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L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
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Figures (8)

Fig. 1.
Fig. 1. Rod and cone photoreceptor visualization on AOSLO. (a) Confocal AOSLO image at 7° from the fovea in a normal subject. (b) Co-registered non-confocal split detector AOSLO image from the same location as (a). (c) Confocal AOSLO image at 3° from the fovea in a subject with ACHM. (d) Simultaneously captured split detector AOSLO image from the same location as (c). Cone photoreceptor examples are shown with magenta arrows, and rod photoreceptor examples are shown with yellow arrows. Scale bars: 10 μm.
Fig. 2.
Fig. 2. Outline of the CNN AOSLO rod and cone detection algorithm.
Fig. 3.
Fig. 3. Creating label and weight maps from AOSLO image pairs. (a) Confocal AOSLO image. (b) Co-registered non-confocal split detector AOSLO image from the same location. (c-d) Manually marked rod positions shown in yellow and cone positions shown in magenta on the confocal image shown in (a) and on the split detector image shown in (b). (e) Label map generated from the markings in (c-d). (f) Weight map corresponding to the label map in (e).
Fig. 4.
Fig. 4. The rod and cone CNN (RAC-CNN) architecture, which consists of the following layers: convolutional (Conv(F,G,N) where F and G are the kernel sizes in the first two dimensions and N is the number of kernels), batch normalization (BatchNorm), ReLU, max pooling (MaxPool(P,Q) where P and Q are the window dimensions), unpooling, concatenation, and soft-max. The same structure is used in the split detector AOSLO and confocal AOSLO paths.
Fig. 5.
Fig. 5. Detection of rods and cones in confocal and split detector AOSLO image pairs. (a) Confocal AOSLO image. (b) Co-registered non-confocal split detector AOSLO image from the same location. (c) Rod probability map and (d) cone probability map generated from (a) and (b) using the trained RAC-CNN. (e) Extended maxima of (c). (f) Extended maxima of (d). (g-h) Detected rods marked in yellow and cones marked in magenta on the confocal image shown in (a) and on the split detector image shown in (b).
Fig. 6.
Fig. 6. Performance of the RAC-CNN method on healthy images. Confocal AOSLO images from different subjects are shown on the top row, and the co-registered split detector AOSLO images are shown in the row second from the top. Rod detection results for the RAC-CNN method with respect to the first set of manual markings are shown on the second row from the bottom, and cone detection results are shown on the bottom row. Green points denote true positives, blue denotes false negatives, and gold denotes false positives. Dice’s coefficients for the rods and cones are 0.98 and 1 in (a), 0.94 and 0.99 in (b), and 0.91 and 0.95 in (c), respectively.
Fig. 7.
Fig. 7. Performance of the RAC-CNN method on ACHM images. Confocal AOSLO images from different subjects are shown on the top row, and the simultaneously captured split detector AOSLO images are shown in the row second from the top. Rod detection results for the RAC-CNN method with respect to the first set of manual markings are shown on the second row from the bottom, and cone detection results are shown on the bottom row. Green points denote true positives, blue denotes false negatives, and gold denotes false positives. Dice’s coefficients for the rods and cones are 0.93 and 0.98 in (a), 0.94 and 0.93 in (b), and 0.89 and 0.88 in (c), respectively.
Fig. 8.
Fig. 8. Performance of the automated algorithms for cone detection in a healthy (top) and ACHM (bottom) image pair. Simultaneously captured confocal and split detector images are shown in the two left columns. Performance with respect to manual cone markings for the RAC-CNN and our previous LF-DM-CNN [31] methods are shown in the right two columns and displayed on the split detector images. Only cones are included in this figure as LF-DM-CNN cannot detect rods. Green points denote true positives, blue denotes false negatives, and gold denotes false positives. Dice’s coefficients are 0.99 for both methods for the healthy image pair, and 0.92 for both methods for the ACHM image pair.

Tables (3)

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Table 1. Average detection parameters across the healthy and ACHM validation groups (standard deviations shown in parenthesis).

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Table 2. Average performance of the automatic methods and second grader with respect to the first set of manual markings across the healthy data set (standard deviations shown in parenthesis).

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Table 3. Average performance of the automatic methods and second grader with respect to the first set of manual markings across the ACHM data set (standard deviations shown in parenthesis).

Equations (7)

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w ( x ) = { L B a c k g r o u n d / L R o d L B a c k g r o u n d / L C o n e 1 l ( x ) : R o d l ( x ) : C o n e l ( x ) : B a c k g r o u n d ,
L = x Ω w ( x ) log ( p l ( x ) ( x ) ) ,
N Automatic = N TP + N FP ,
N Manual = N TP + N FN ,
True positive rate = N TP / N Manual ,
False discovery rate = N FP / N Automatic ,
Dice s coefficient = 2 N TP / ( N Manual + N Automatic ) .